{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling and Simulation in Python\n",
    "\n",
    "\n",
    "Copyright 2017 Allen Downey\n",
    "\n",
    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Jupyter so figures appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "\n",
    "# import functions from the modsim.py module\n",
    "from modsim import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Low pass filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A 0.0 dimensionless\n",
      "Bd 0.0 dimensionless\n",
      "Bq 0.0 dimensionless\n",
      "C 0.0 dimensionless\n",
      "Gy 0.0 dimensionless\n",
      "Hz 0.0 dimensionless\n",
      "K 0.0 dimensionless\n",
      "Sv 0.0 dimensionless\n",
      "amp 0.0 dimensionless\n",
      "ampere 0.0 dimensionless\n",
      "ampere_turn 0.0 dimensionless\n",
      "baud 0.0 dimensionless\n",
      "becquerel 0.0 dimensionless\n",
      "bit 0.0 dimensionless\n",
      "bps 0.0 dimensionless\n",
      "candela 0.0 dimensionless\n",
      "candle 0.0 dimensionless\n",
      "cd 0.0 dimensionless\n",
      "coulomb 0.0 dimensionless\n",
      "count 0.0 dimensionless\n",
      "counts_per_second 0.0 dimensionless\n",
      "cps 0.0 dimensionless\n",
      "degK 0.0 dimensionless\n",
      "delta_degC 0.0 dimensionless\n",
      "fine_structure_constant 0.0 dimensionless\n",
      "g 0.0 dimensionless\n",
      "gram 0.0 dimensionless\n",
      "gray 0.0 dimensionless\n",
      "hertz 0.0 dimensionless\n",
      "kelvin 0.0 dimensionless\n",
      "lm 0.0 dimensionless\n",
      "lumen 0.0 dimensionless\n",
      "lux 0.0 dimensionless\n",
      "lx 0.0 dimensionless\n",
      "m 0.0 dimensionless\n",
      "meter 0.0 dimensionless\n",
      "metre 0.0 dimensionless\n",
      "mol 0.0 dimensionless\n",
      "mole 0.0 dimensionless\n",
      "rad 0.0 dimensionless\n",
      "radian 0.0 dimensionless\n",
      "rps 0.0 dimensionless\n",
      "s 0.0 dimensionless\n",
      "sec 0.0 dimensionless\n",
      "second 0.0 dimensionless\n",
      "sievert 0.0 dimensionless\n",
      "sr 0.0 dimensionless\n",
      "steradian 0.0 dimensionless\n",
      "stere 0.0 dimensionless\n",
      "Δcelsius 0.0 dimensionless\n"
     ]
    }
   ],
   "source": [
    "with units_off():\n",
    "    for i, name in enumerate(dir(UNITS)):\n",
    "        unit = getattr(UNITS, name)\n",
    "        try:\n",
    "            res = 1*unit - 1\n",
    "            if res == 0:\n",
    "                print(name, 1*unit - 1)\n",
    "        except TypeError:\n",
    "            pass\n",
    "        if i > 10000:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.998 dimensionless\n"
     ]
    }
   ],
   "source": [
    "with units_off():\n",
    "    print(2 * UNITS.farad - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1999.0 dimensionless\n"
     ]
    }
   ],
   "source": [
    "with units_off():\n",
    "    print(2 * UNITS.volt - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1999.0 dimensionless\n"
     ]
    }
   ],
   "source": [
    "with units_off():\n",
    "    print(2 * UNITS.newton - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "gram meter/second<sup>2</sup>"
      ],
      "text/latex": [
       "$\\frac{gram \\cdot meter}{second^{2}}$"
      ],
      "text/plain": [
       "<Unit('gram * meter / second ** 2')>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mN = UNITS.gram * UNITS.meter / UNITS.second**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 dimensionless\n"
     ]
    }
   ],
   "source": [
    "with units_off():\n",
    "    print(2 * mN - 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'll create a `Params` object to contain the quantities we need.  Using a Params object is convenient for grouping the system parameters in a way that's easy to read (and double-check)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R1</th>\n",
       "      <td>1.000000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1</th>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.000000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "R1    1.000000e+06\n",
       "C1    1.000000e-09\n",
       "A     5.000000e+00\n",
       "f     1.000000e+03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = Params(\n",
    "    R1 = 1e6, # ohm\n",
    "    C1 = 1e-9, # farad\n",
    "    A = 5, # volt\n",
    "    f = 1000, # Hz \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can pass the `Params` object `make_system` which computes some additional parameters and defines `init`.\n",
    "\n",
    "`make_system` uses the given radius to compute `area` and the given `v_term` to compute the drag coefficient `C_d`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_system(params):\n",
    "    \"\"\"Makes a System object for the given conditions.\n",
    "    \n",
    "    params: Params object\n",
    "    \n",
    "    returns: System object\n",
    "    \"\"\"\n",
    "    unpack(params)\n",
    "    \n",
    "    init = State(V_out = 0)\n",
    "    omega = 2 * np.pi * f\n",
    "    tau = R1 * C1\n",
    "    cutoff = 1 / R1 / C1\n",
    "    t_end = 3 / f\n",
    "    \n",
    "    return System(params, init=init, t_end=t_end,\n",
    "                  omega=omega, cutoff=cutoff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a `System`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R1</th>\n",
       "      <td>1e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1</th>\n",
       "      <td>1e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>init</th>\n",
       "      <td>V_out    0\n",
       "dtype: int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_end</th>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omega</th>\n",
       "      <td>6283.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cutoff</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "R1                          1e+06\n",
       "C1                          1e-09\n",
       "A                               5\n",
       "f                            1000\n",
       "init      V_out    0\n",
       "dtype: int64\n",
       "t_end                       0.003\n",
       "omega                     6283.19\n",
       "cutoff                       1000\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system = make_system(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the slope function,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def slope_func(state, t, system):\n",
    "    \"\"\"Compute derivatives of the state.\n",
    "    \n",
    "    state: position, velocity\n",
    "    t: time\n",
    "    system: System object\n",
    "    \n",
    "    returns: derivatives of y and v\n",
    "    \"\"\"\n",
    "    V_out, = state\n",
    "    unpack(system)\n",
    "    \n",
    "    V_in = A * np.cos(omega * t)\n",
    "    \n",
    "    V_R1 = V_in - V_out\n",
    "    \n",
    "    I_R1 = V_R1 / R1    \n",
    "    I_C1 = I_R1\n",
    "\n",
    "    dV_out = I_C1 / C1\n",
    "    \n",
    "    return dV_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As always, let's test the slope function with the initial conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope_func(system.init, 0, system)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then run the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The solver successfully reached the end of the integration interval.\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sol</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_events</th>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfev</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>njev</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nlu</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>message</th>\n",
       "      <td>The solver successfully reached the end of the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>success</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "sol                                                      None\n",
       "t_events                                                   []\n",
       "nfev                                                       86\n",
       "njev                                                        0\n",
       "nlu                                                         0\n",
       "status                                                      0\n",
       "message     The solver successfully reached the end of the...\n",
       "success                                                  True\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = linspace(0, system.t_end, 301)\n",
    "results, details = run_ode_solver(system, slope_func, t_eval=ts)\n",
    "details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the plot of position as a function of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NixP6RyD3Qanqo8CjJS9PAnvPMQZVTQH3nOVzjwA/Vb2ZVhdP/GmDP4sfmIA6X7LZLGOTxSKumwb9VCK6mF1wylGdmlxko0/CsNFwCws/XOR5hvs7iUYiZLJZpuYSJFZSVSl3ZXgJnAXVrKQzWa/7aEP1g7t5SgWUZSGVh9t91N7WQl/3+bdAORcjg24N3ZJZyuXUhL9JRnlaW6IM9Rc36I6ZoucLJqACwtRsglQu1tDd0Xpe7d3LpberjY64o+2tJNNMWRZSWZxyuY82Dnb45j4CrwvJrNzySKbSjM8UMyz9csHmGRm0OJTfmIAKCO4fuN8PViQS8Wjop8yHXhbuezTio3sPYLi/w1P1I9+51zg7Y1NLBW/AYE+cNh8yLN1YHMp/TEAFhNNTxR/48ID/8YZNQ27tz1xI5VBLJSIWi3p+B6ahnxu3K3STj+69wneYq9x3TEAFhLEpd/C9BgLKEiXWRTqdYXy6eI9qkbTguUcWhzontUqQyNPfHS9s2E2spJiZtx5r1cYEVABIpzOMzxQXv+GB6hWIPRsbBzsLMZSJmQTJlO21WYvxmQTpjKMhu2N4fuIWUCdNiTgnbi9ELZS80jiXeSKqjwmoADAxkyCTW/z6uuM1SVeNt8YKTdwy2Synp20BXIsxj3vPf/cReLPQxsyFtCaLiWShC0BLLMpAT21aYFiihL+YgAoAYy7Nb2MNrKc87lYebveVcSZuAb5psDb3qKeztaCsLCfThX1RxpmcdrnIN/R3VLULwFq4vR32DFUfE1ABYKzGCRLF7yo+XO4H3DiT0gWwFkQiEe89sgXwrLj/barZQ+1cuJ/X09NLZuVWGRNQAaDWCRJ5NpgFVRbpdKbQ5RZqJ6DAu9iaEnF2vFmwtbs/Xe0thXhkMpVhet72FFYTE1B1JpnKMOnaXFhL7c+90E7MJqwo6VmYnF0uxAh7u9pqWtLGa0FZjONsuC2ojTX0Qpxh5ZoSUVVMQNWZiZklMtl8gkSb75sL3bS3tRSKkmYyWSZnTftbDbdgqKULFpyab4V5TJkLaTUSy6lCfC4WjTDQW5sEiTyee2SeiKpiAqrOuCtVu3/otcLjQjINfVXcWnEtLVzwKi1LyykWlqx/VyluobDBVYGjVpgF5R8moOqM9+GqrebnfKfFoc5FvQLwcKbb1zT0M/EkGdX4/pR+57glSlQVE1B1ZmK69tlhbjZYEH5NMpms5x7VMgC/2nfaPToTj4VbYxcsOHHJfGflxEqKuUWzcquFCag6ks1mPRUkNvTVY/ErPtDjM6b9lTIzv0wylzzS1d5KZ7t/VebPhjeTz9ywpYzX0cKF1RIl7B5VCxNQdWRmfqVQYqgj3kJne+0bnlma7NqUxjfqgVuJGDMLykMylWYmlyARjUQY7Ku9mxwsUcIvAtkCUkRuBj4GxIFngA+q6mw5Y0QkBtwH3IRzffeq6gMl790FfB+4UVW/5/f1nA2P9dTvb3+hs5GPcRw5NefMaXqpZmViwsDpOrv3wClK2hKLkkpnWEgkWUwk62LJBZGJmUTB6u/vcf6d6oE7fjxhAqpqBM6CEpFhYB9wq6oKcAj4+DrG3AXsAS4HrgHuEZFrXe9tBz4D+NcOtUzGA6CdAwy5vnvKUs09TNTZBQsQjUYYdKVOuzM/mx33v8VQne4PnLmn0KgOgRNQwI3AAVU9mDu+H7hdRCJljrkF2KeqKVWdAh4C7nC995PAp4FxH6+hLDwJEnVyTQAM9bkXP9P+3Ey6F8A6ZFkWvtt1jyZNQBVwK3lDdXyG+nvaC/X/ZhdWWE6m6zaXRiKIAmo7cNR1PAr0Aj1ljlnt3DYAEfkloFVVP1X9aa+fIMQ3ANPOz0JiOeWpkN3XFa/bXNzW28SsKRF5vPsI6/cMxaIRj2vclIjqEEQBFQVWSyVLlzmm9FwESIvIG4C7c//VHffiV/rjrjWDve2F+NfMwgopK3kEeF01A73xmlXIXo3BPlMiSslmsx6Lv54WFHi9IOPmiagKQRRQR4AtruOtwJSqLpQ5pvTcFhwr6n04Vta3ROSp3OufFZH3VP8Szo178Rvsa6/r4udYB05ILpvNMmk+dMDr7hzqrZ92Dme6+PK1AZuZucVkwZUWb4vR1VHfxBF3DMyUiOoQRAH1GHCdiOzOHd8N7F/HmP3AnSLSIiL9wG3AI6p6j6ruUdW9qroXOA7crqpf8PVqzkKQFj+wGMdquBeZelT5cNPp2oOVTGesNxRnJrDUIwvWjfcZMguqGgROQKnqGPAB4GEReQG4AviQiFyds3zOOib3EfcDrwBPAweAB1X18RpfxjlxC4F67d1w49H+zIICgpMhVpyDJbO48SgQgbs/Cdv0XgUCuQ9KVR8FHi15eRLYe44xqGoKuKeM79hZ2Swrw+1Gq7fvHEoTJWzxC1p8Iz+Ho7n9ahOzCS6q83zqjef+1NnCBejqaCXeFmN5Jc1yMs38UpKezrrvZgk1gbOgmoFsNuuxUoZq3B5gNQbNxedhdsFb5SNfbaOeuF3BthkUxqeDZeFGIhHPPbLiy5VjAqoOLCRSLK84wd221voHd8GpVpBvUzC/lCSxkqrzjOqL18Ktf3wDvFZCswfh0+liWa5IxLuRuZ5ssHtUVUxA1QF3ANWd4l1PoiWN3po9k88bfwrG4le6HSBv4TUj0/PLhRhPT2crrS3BWMosk6+6BOOuNhlBiz/lcbsam93NF5QKBW5aYlH6um07AMDUXLEkV5BqR3oy+Zr4/lQLE1B1wP3DDYprAmwzqJugZfDl8VSUaOJklmm3gOqtX4WPUvp7inOZnrP9apViAqoOuBe/IAkoc084pAIa34AzU5mbFbeSFyQLqr2the5cTDmdyTKzYMWXK8EEVI0pdc0ExX0EXmE5Odu8+zjc197X1RaY+AaYEpFnas4toIJjQQGeWK51B6iM4Dx5TcLcYrIQ3G5vC0b6ch53sDmxkmIx0ZyZfJMBTJDIU7pZtxmViGw2y/Ss28UXrHs02GNxqGphAqrGlFpPQcjgyxOJRDwaerM+XEGNPwH0uiy6peUUS8vNp0TMLyVJpoOp5IE3JjbVpM9QtTABVWMmAxp/ymPldLztLIJQhspNaUysGZUIb5JRsNx7cKar3Dh/TEDVGPeiH7TFD0rdE83pP3fHDYKoRAw2eYwjyO49KLk/c8uWyVcBJqBqzGTAShyV0t/k7olkKs3colMpPBqJ0NcdPA292TdUBzlBAqDdVRorlc4Ufk/G+jEBVUMymWxg90DlKdX+mi0I77ZI+nuK5Z+CRLO7kIK6SddNs9+jamECqobMLCyTzpn7Xe2ttAcsuAvQ3eHN5Gu2IPxkwLVz8M6rGRc/9zX3B/QeNbsbtlqYgKohQesBtRqRiLf9vFtbbQamPG3eg3mPervaaIk1ZyZfwnW9LbEovV3BbGdhFlR1MAFVQ9yLfRDde3ncGnqzxaEmA54gAXklojnvkfsZ6u+JB2qbhhtPqvlc89yfamMCqoZ4tPOAuiaguXfCTwW0hE4pzaqhexMkwnN/mi2WWy1MQNUQT3A3oNo5lFhQTaT9pdMZZhacjKtIJBKoIqSlNKsSMRXQIrGldMRbaG9zYszJVIaFpWSdZxROghelB0TkZuBjQBx4Bvigqs6WM0ZEYsB9wE0413evqj6Qe881wB8DXUAM+ANV/UwtrimbzQY+PTZPs2rnpT2G8nGeIOK5R02kRITFC+FsqI5zfNyJl03MJui29u/rJnBPoIgMA/uAW1VVgEPAx9cx5i5gD3A5cA1wj4hcKyIR4B+AD6vqXuBdwB+JyO4aXBYLS8GtwVdKb3ecaKTYXTeZStd5RrUh6FsA3DRrOZ0wpJjn8Vq5zXOPqkngBBRwI3BAVQ/mju8Hbs8JmHLG3ALsU9WUqk4BDwF34FhaH1HVLwOo6ihwGtjm+xVR2h4guMFdgFg04knfbRYX0lTAKxS46esq7tGaX0qynGx8JSKVzjDrcsEGNcU8j1VlqZx1qfE5a+OXgLfiLOxp4CjwJeCzqvpKFea0PfeZeUaBXqAHmC1jzGrnrlTVBPCg61p+OTf+O1WY8zkJi+88z0BPvCBUJ+cSbBzsrPOM/Mftgh0MuHYejUbo744zkbtHU7MJRoa66jwrf5kJkQsWvFtJzII6P8oSUCIyCPwJ8DbgC8BfACdw4jibgeuAr4vIl4APqepEBXOKAqulvKTLHFN6LlLyXkTkN4FfA25S1ZpURA1Ldliegd52ODYDNI8F5d6nFgYlYrCvvSCgJptAQIXJBQslJanmnEy+IHtOgki5FtR+4I+AX1TVzCrnHxSRFuDncQTYmyuY0xHgTa7jrcCUqi6UM0ZEjgBbXOe24FhRiEgc+DRwGXC9qr5awTzXRVgy+PI0WyZfJpMtdNGFkNyjJsvkC1P8CaCrvYW21hgryTTLK2mWllN0trfWe1qholwb+SdV9fNnEU4A5GI+fwP8eIVzegy4zpW8cDeOgCx3zH7gThFpEZF+4Dbgkdy5z+C4Am+opXCC0ocr+Nr5QJM1XXOXoeruaCXeGqvzjM5NszXG88YIg/8MnbGhusmqslSDsgSUqiYBROQ3VjsvIv+1dOz5oqpjwAeAh0XkBeAK4EMicrWIPLXWmNxH3A+8AjwNHAAeVNXHReR64OeAi4FvishTuf/eWcl8y8HpTuv8s8SiEXpCkG7qXgBm51dIp8+qmzQE0yGzcKH5qhVMh2STrptmr5tYKed08YnIRpwYE8BHRERx4jp5+nDiOf+5WpNS1UeBR0tengT2nmMMqpoC7lnl9W/jnXfNKM0OiwawQnYprS0xejrbmFtcIZPNMrOwEgq///ni7qIbBgsXoD+3HSCTzTK7sEIylaa1JfiW3/ng7CMMlxcCLNW8UsqJQc0CvwNsBNqBPy05vwz8fpXn1VCEZYNuKQO98UIvm8nZREMLKLd2HpbrjMWi9HXHC7+vqdnlhs22nFtMkspZ8R3xlkB2AliNZi68XA3OeZdz6dlvAhCR/62qP+v7rBqMsAV38wz0tHPk5BzgdYE1IpMh2gPlZrC3KKAaeTtA2LJg8zTrhupqUW6aeW/uz/e7/vZQWorIKOJt4RAeC6pZSh6FqQxVKf097UDjbwfw3J8QPUO9XXGi0QiZTJb5pSQryTRtIUjACQrl2snTnLm3yE0WZ0+UsQrhtaCaQ/ubLylDFaZUYPdiPd3AiRKeVjUheoZiuQ3VeQVveq5x3bB+UK6A2uXrLBqYsJVncTOwSvv3RtxoGLYNoG6apZyO2zrsD5EFBc1ZlaValCWgVPW1/N+5dO3345QUOgV8RlW/4svsGoDpuXCVZ3HTEXeK2i4tp0ilM8wtJgPbwbQSwuqCBe98Z+advVyxEGSJrpew9IFajWasylIt1rVaisitOJtkM8DjwArwiIj8gg9zawimQpgd5saThdSgbj5PF92QLX6tLTG6OxyXZCabZXa+8RZAd1v71liUns7wuGDB6ypvZDesH6w3V/N3gZ9W1a/mXxCRzwF/DvxtNSfWKIQ1/pRnoDfO8fF5wBG2F2xeNUcm1ITZggJHQ5/PNcSbnE2EKguxHNxKXpDbvJ+NgSZxw/rBev1NO4Gvlbz2NZxaeMYqhH3xa/QYRzab9TT8C6OVO9jge23C1AZlNVZzwxrlsV4BpTj9ltz8LPBSdabTeITdgupv8CyxpeUUyytOsfvWlihdHeFyH4H3HjWiGzasWwDyNIMb1i/W6+L7beAfReRO4DUci+qtwE9XeV4NQSaT9dZ4C+HD5d0L1XgPVmkGX9jcR+C9R2ZBBZPBBnfD+sW6LKhcN9qrge/n3vsE8HrL4luducWVUJZncdPd0UprLvMwsVIMVjcKnsUvhBYunNkaJZ812iiE3YKCM7dsGOWx3o66b1XVr+MkSxjnYDrk7j3I7d3qjXN6yunrODWboGO4u86zqh5h3gOVpyPeQrwtxvJKmmQqw8JSku4QVMwvh/z2Bsj9FrtDKqAsk++8WG8M6u9E5JCIfFRELvJlRg2Ed/EL54MFjR2E9zaSDOc9cvoONeY9cu8j7OtqIxaifYRuBhrcVe4X673bW4BfwYk9/VBEvikid+UaAxolhD1BIo/XPdFY2t9kSIuQluJWgBqpbqL0UVGzAAAgAElEQVT3/oRTgYDGd8P6xbpcfLmOuv8C/IuIdOBk8P0+8MdAR/WnF27cGVVhK8/ipt9Tk69xtL/SRpJhrpLR36Abqt1u8v6QumChsd2wfrLuqL2IRICfAP5PHAF1EPjDKs8r9JQ2WAtrfAPO1P4ahTA2kjwbjdpa3FOJJcQWbiQSYbCnnRMTC4Bzj0xAnZv1Jkl8Avh5IA18FrhBVV/0Y2JhZ2k5RWIlV56lJVrYBxFG3J1b5xadyt+tLeGMBbgJc323Uho11bwRYoR5BnrjBQE1OZtg+6aeOs8o+KzXghrGKRT7ZVX1zYkqIjcDHwPiwDPAB0v7TZ1tjIjEgPuAm3Cu715VfSD3nt3Ag8AGYB54n18C1uOaCGF5FjexWJTerjam552A9fTcMsMD4ffoToa8yoebns42YtEI6UyWxUSSxEqK9rbwbWtwk8lkvW7yEMegoMQN20BKhJ+UpQaLyJ+ISL+q3qGqXzqbcBKRIRH5ZCUTEpFhYB9wq6oKcAj4+DrG3AXsAS4HrgHuEZFrc+c+CzygqpcBHwYezrksq44ngy/k2jk0ZqKE28UXZhcsQDQa8d6jBogVzi2uFMoCdba3hl7geqzcBokTJlMZnnz+JM++PE7GhxJO5fppvgJ8R0Q+JSLvEJG+/AkRGRCRd4vIXwJPAv9a4ZxuBA6o6sHc8f3A7SWCZK0xtwD7VDWlqlPAQ8AdIrIVuCR3jKr+M9ANXFXhfFdlJtcDCsK7+92Ndx9H+Bc/aIwNoG4aLVY4FfIqLKU0YpzwqZfGePK5kzz+w1FO5tyX1aQsAaWqX8CpIPEi8KfApIgsiMgSMI7jansBuCI3thK2A0ddx6NAL9BT5pjVzm3LvX48l4lYeq7qDPc7LrBoJMKuLeGvAO6tyBz+xS+ZShcaSUZDvAHUTaPthZpqkBTzPHk3LFBww4ad4+NFoeRHlZmybWZVnceJ7dwnIptwFvwMcFRVT1dxTlG87eXzpMscU3oucpbX3eeqzp4dA/R0thFvi4XefQTeGE1DLH6ua+jtDu8GUDcDDVY01mPhNsAzlHfDjk87VVmm55YZGQq323LK50os5/Wvo6qncLrp+sER4E2u463AlKoulDNGRI7gbCjOswXHUjoCbBaRiCuGlj/nC5s3dPn10TXHvUBMzyXIZLKhTsv2+8GqB41nQTVOjDDPQE+8IKAmZxOMDIV3jVhJpgsFcKORCL0+eCGCqDY+BlyXy7gDuBvYv44x+4E7RaQlV+HiNuARVR0FXgbeCyAi78SxAJ/17UoaiHhrjK52J1U+nckyt7hyjncEm8kGKBJbijtbdHahWKg4jJTuI2wEFx80lhLhjkX3dccL7stqEjgBpapjwAdwMuxeAK4APiQiV4vIU2uNyX3E/cArwNPAAeBBVX08d+4XgLtF5Ec4FTD+fUlMyliDgQYqp+PZABryFPM8La526PntAGGldB9hGPt0rYb7GZq2Z+icBNIBqqqPAo+WvDwJ7D3HGFQ1Bdxzls89CPx41SbaZAz0tDM6lm//vsyuOs+nErx7oBrDggLnHuWTP6bmEmzoD+d+tdJOAGHeR+jGk2wUYgUCvBZgv09eiIosKBHZV3JstTsamIEG6a6bTmeYnXdtA2gQ9xE0TkWJRikSW0ojuWGnarDR/bwsKBG5AScxYXfJqX8BfrLSSRnBxJtqHt7Fb3p+mUyumnRvVxutLbE6z6h6NEomn7fEUeNYuHk37OzCCtlslpn5ZYb6wmnl1qJbw/m6+OaAy4BLReQHwCzwPE4JIaNBKa0mkc1mQ+l6aYQuumejUYLwjbaJ2s1gb9ENOzmbCKWAymSyTM/7n8RyXi4+VX1WVT8C/JyqvgF4D/C3wM3VnJwRLLraW2hrdayN5ZV0aNu/T841Xop5ntKKH36Un6kF0w1qQUFjKBEzC8XfVndHa2FdqDaVJkkMiciFqnoI+EY1JmQEF6dza5xTk4uA83B1tocvu6oWvvN60R5voSPewtJyKtcufYW+kFXJKK3y0RfiPl2r4XXDhlNATdcgQQIqTzO/FPiEiDwnIl8Rkf9ejUkZwcVTTyykMY5G6dN1NsKeKDE9V0xgaZQqH268FlRInyHPJmr/FKCKLChV/b383yKSb2JoNDBhbxnQaC0cVmOgJ86x07ntALMJdm4OVy3IRurTtRqlbtgwxnK9WZbBtaAKqOq/4V/5IyMghN2Cml1orBYOqxF2Db3RisSWknfDAqTSmYI7M0y4f1d+KnkVPZ0i8jxwAvg+8CrwDuA/Vz4tI6iE3X001cAJEnnCHuOYbHAXLDjXlbdyp+eWQxUnLK1S4mcSS0UWVK7x30dw6tm1AP+hGpMygktvrv07OA3lkilfisH7xsRM45U4KsW9qE/mtgOEiekGd8FCuHt3LSZSLCed576tNUZXu39eiEotqD/D6V6bBjqBz1djUkZwiUUj9HXHCw/V1NwyGwc66zyr8mnEKualdHW00toSJZnKFLYDhCXb8oz9NQ16j8K86b10j5qf8bNKY1BXqeo7VfXdOMVa76/CnIyAMxjiagWNvAcqj7MdIJyuWHeMsKu9lbhP+2vqTZgrfng3uvtr4VYqoGIi0gGgqt/H6WprNDhhzeTLZrPeh6tBBRSEN5ml0ZoUno2BEMdya3mPKnUe/gHwmIjcD7Szepdbo8EIa3ddd3HODlcmVSMS1gWwEXtArUa3yw2bWEmxmEiGxg3rqWLuc3LHOS0oEfndvJVUiqo+AtwJ7AR24PRbMhocj/soVNp542eH5QmtBdXAVT7clLphw9S7yxPH7au/BfV+4JdF5L8An3a1SwcKPZb+mw9zMwKKZ6Ph/HJo2r9PNkGCRB63BRWm5pK1qJAdFAZ64oxNFcuGbRnurvOMzs2yu817NEJfV/1jUALcB/wh8JSIvMPXGRmBp601Rneuw2kmk2VmIRzaXzNk8OXpc20HmF9KhmI7gNPmvTliUBBOJcJj4XbHfVdMz2lBqWoSp97eg8BvAY+IyDeA31DVH1V7QiJyM/AxIA48A3xQVWfLHSciMRyBehPO9d2rqg/k3nMN8MdAFxAD/kBVP1Pta2gGBnrbC5rU9NxyKLTdySZxH4GzHaC3u63gOpqaXWbjYLC3Aywtp1heqc3+miAQxr1QkzV078E6svhUdVZVfwtn39Nx4Psi8ikR2VytyYjIMLAPuFVVBTgEfHyd4+7KzfFy4BrgHhG5VkQiwD8AH1bVvcC7gD8SkdKmi0YZeGMcwbegstlsU7n4oLTqR/AXwNIEibDVp1svbgsqLDGoWmfBrjvNXFWPqeqdwFXACPCSiHy4SvO5ETiQi2uBs6/q9pxwKXfcLcA+VU2p6hTwEHAHjqX1EVX9cu46RoHTwLYqzb2pCFu9t4WlJMmUk8HX3tbYGXx5wrYZtFHbvJ+Nvq62ght2diEcVVk8Sl4NvCbrfkpzGX2X4LTaeAbHUvldnJJH5X7Gu4EvrHLqo8BR1/Eozt6qHpyuvXm2rzFutXNXqmoCeNA1h1/Ojf9OufM2irhdZGHwn094rKfG187Be4+mQ6BEeKvMN76FG4tFvW7YEFRl8cYI/VcizimgROROHGF0We7/O4AI8BrwHPC53P/LRlUfXe27ReS3WX0vValqEV1jXOm5SOn7ReQ3gV8DblLVpfJnbuQprVQQ9JYB3vTlxl/8IHydW91W3lAN4htBYKCnvSCgpgMuoEobSfq9BwrKs6D+X+BHOELo73P/f15VF3yYzxHgTa7jrcDUKt911nEicgTY4jq3BceKQkTiwKdxhO31qvpqVWffRHS2txBvi7G8kmYlmWZhKUl3Z3A7n9baNREESvsOpTNZYgHeDjBVox5DQWKwN87h487fQfdEuONPfd3xmjSSLCeLb4fvsyjyGHCfiOzOxZfuBvavc9x+4E4R+UegG7gtdx7gMzhFbW/wScA2DZFIhKHeDo6POy0DJmYTARdQrk26TaKd57cDzC8lyWSzzC4EN9sysZJiIeFkhcaiEXobrM372QhTxQ9vHcvaxAgD1UtZVceADwAPi8gLwBU4RWgRkatF5KlzjcNJmHgFeBo4ADyoqo+LyPXAzwEXA98Ukady/72zdlfYWLh/pO42FkHDqcHXfC4+KFkAA5woUZodFoaN39XAU00i8BZU7Z+hwKUy5eJTj67y+veAvWWMSwH3rPL6t3HiUUaVGOorVsAKcjmdBVf/mngT7K9xM9AT5+ipOcBxIV24ta/OM1qdWrUQDxqe7RoBr8ri8ULUSEAFyoIywoXbVRZkC6pU8wtyMke18e61CfA9cs2tWRIkIFxVWepRicUElHHelBYkDWrn1snZ2vvOg4K3WkFwF7/JmebaA+WmPwRFY1PpDDO5DL5IJFKzTscmoIzzprO9tbDhNZnOFFJQg0Yz1eArZbD3zO0AQaTZqny48TYADaaAmnb9dvq62mipQQYfmIAyKsTtjglqmuxkkzQpXI2OuLMdAChsBwgaKyUVsntrsL8mSHgrfgT1GapPkpEJKKMi3NpuEONQzViDz00Y2r9PllTIDvJeLT/oD0HR2Hq5yU1AGRUxGPCWAUvLKRIrKQBaW6KFgHQzEfSq2bUuQBo0wuCGrdc2DRNQRkW4U82DKKDcVt1gk2Xw5fH2HQqgBeXeANpEGXx5OttbiLe63LCJVJ1ndCaeFPMabgMwAWVUxECvN5MvkwmW9ufODmum9GU3His3gG5Y95yapQyVm9KsuKDtKUylM8zMOwIqEonUtJeaCSijItrbWgpus3QA93FMzBZrAQ/1dqwxsnHxxAlnlwLnQqp1hewgEmRX+dTsMpncb6a3q43WlljNvtsElFExQU6U8Lj4mtSC6ulspS3nQlpeCZYLqR4VsoPIBperfGImWA0W3Erehho/QyagjIoZDGiqeSaT9QioZnXxOYV93UpEcBbAelTIDiJBrsrifYZq64Vozl+DUVWCGuOYXVghlXa66Ha2t9LZ3nwZfHmGAroA1qNCdhDx7CecCVYs163Q1NoLYQLKqJig+s/dD1azWk95PNmWgbKgmrPKfCluBSpoVVkmpuvnhTABZVTMoKcg6TLpnNVSb8y9VySoFlTpNoBmxh3fGQ+IEpFYLvbpaolF6euqrZVrAsqomLbWWKHBXCabDUy1Ao8F1aQZfHlK44RBcSGNT5uVm2eo323lBkOJmCipwlLrViAmoIyq4HYhBUX781hQ/c29+JVuB5ier78SkVhJFWrwxaIRT1XvZmQogBZUvd3kJqCMqjDs0v7cWnG9SKbSnvYAze4+gtJMsfrfo8kS916z1eArxW3lB+EZgvq7yU1AGVXBo/1N1989MTnrag/QXbv2AEFmyLPXpv73qN6LX9AY7I0TzZXiml1YYSXXBbqeeF2wtXeTB673tYjcDHwMiAPPAB9U1dlyx4lIDLgPuAnn+u5V1QdK3rsL+D5wY66VvFEhG/q9Gw2z2Wxd6965LYQNdXiwgkjQEiXGZ+q7+AWNWCzKQE+8EPeZnE0wMtRVt/mUdgJoegtKRIaBfcCtqirAIeDj6xx3F7AHuBy4BrhHRK51vbcd+AzQ5uOlNB1OCRTn57S0nKp7tQLTzs/E7UIKgovP7tGZDAbIyp1dWCGZcjJyO+ItheaktSRQAgq4ETigqgdzx/cDt4tIqSq+1rhbgH2qmlLVKeAh4A7Xez8JfBoY9+kampJIJOIt11JnH7r74XZbd81MkFxI2WzWa+XaPQJgQ7/bVV7fZ8hrPXXUxSNSFwElIu8WkVTpf8BFwFHX0FGgF+gp+Yjta4xb7dy23Pf+EtCqqp+q6gUZgDdNtp5ZSKWLnyVIOMRiUU/V7Hpuqg6Cdh5ENgTIggqChVuXX4WqPrrad4vIbwOrbdAoVfWia4wrPRcB0iLyBuBu4K3nM2fj3GwISKLE0nKKpeVik8L8Hi3DWWjygmlipn4xjtL6bs3Yp2s1hkoyLesZyw3CHrWgufiOAFtcx1uBKVVdWMe40nNbcKyo9+FYWd8Skadyr39WRN5T3UtoXkoTJeqFLX5nZyggVbPHPe49s3DzdHW0Em/LVZ5Ppgv7xOpBPYvE5gmagHoMuE5EdueO7wb2r3PcfuBOEWkRkX7gNuARVb1HVfeo6l5V3QscB25X1S/4djVNxlBfsWPt9NxyoVBrrTkdAM0vqAQlk88do2z2Kh9unMrz9d8PVdqksF6FfAMloFR1DPgA8LCIvABcAXwIQESuzlk+a47DSZh4BXgaOAA8qKqP1/RCmpTWlhh9rpJH9VoAT08VH+phC757KK34Ua/mhW4lwhIkvLgtyno9QxMziUKTwr7u2jYpdBO4yGQuPvXoKq9/D9hbxrgUcE8Z37OzookaqzI80FEoo3N6apFNg501n8Pp6cXC37b4eenpdFxIyytpllecZoF9NW4SmFhJFZsURuunnQcV92/29NTiGiP9w/29w/21f4bzBMqCMsLP8EDxxzw2VXv3RDKVZma+2KHVBJSXSCTiWXDq4ULyBN9725u2SeHZcN+f03Vy8bm/d3igfs+Q/TKMqjJcZ+1vfDpRcFsN9MStxNEquBeceiyAHhdsHRe/oDLYV6xLOLuwQmK59pveg+Imt6fXqCruBWdiNlHz3lBu954tfqvjVSLqa0HV030UVGJRr+U/VmNFL53x7iM0AWU0DO1tLYWYRiZT+0QJ94Jr7r3V8QioelhQAXEfBZl63qOp2QTpXL+w3q422uu4idoElFF1Ng7UT/vzLn6mna9GX3e8UDdxMZFkoYZ7bZKpdKGhZSQSsW0AZ6GesdwgKXkmoIyqU68gbzqd8fQYqvfDFVSiUW/dxFomSkzMeGOE9UpfDjobXQKq1rFcrwvWBJTRYAzXyYJy793o7Woj3mqL39moV6JEUILvQWewN163RAlvHLe+XggTUEbV8SRKzNQuUcLce+XjsXJrqETYHrXyiMWink3VtVIistlsoDZRm4Ayqk57W0uhQGstEyXGPJsLbfFbiw11CsKPBSi+EXQ8Vm6N4lDTc8ueKvNd7fWt5WACyvAFdwWJU5O10dDd31OPChZhonSvzWLC/0SJZCpdUFYikYjdo3PgjkOdmiytl+0P7mdoZLCz7oWWTUAZvjAyWGzjcHLC/4fLFr/1EYtGPG7QWigRp6eWPAkSbRYjXJORoeL9OTmxWJO6iSfdSl4d283nMQFl+MIm18N1ogYCyha/9VNrK/ekWbjrYqCnvfA7Xkgka9J645TrWQ3CPTIBZfjCcH9HTV1Itvitn1oLKHPBro9oNOJx8/ntiUimMh4vxMYA3CMTUIYvxGLRmrqQxmzxWzelAspvF5JXO6+/+ygMlLr5/OT09GJhm8ZATzwQ2zRMQBm+4X24/NX+3Bl8tviVR29XGx25MjYryWKFBz+YXyq6qFpjUasgUSYjQ7WL5Z6aCJ6SZwLK8A3vw+Wf9reYSBb6C7XEogza4lcWkUiEEbcV5eM9cltPwwOdRKP1zQ4LC+77c3p6ydcu1UF0wZqAMnzD/XCNTS6SyfjjQnI/WO7Yl3Fu3JlafqYyexa/oWAsfmGgPd5Cf0+x+LKfZam8AioYXggTUIZvdHe20d3RCkAynfFtw67bOrPFb33UKlEiiNp5WHAren65+RYTSeYWi16IoLhgA9fyXURuBj4GxIFngA+q6my540QkBtwH3IRzffeq6gO59wwCfwZcBnQAv6+qf+3/VTUvI0NdvDw6DcDx8Xlf2iucGC8+tCMB0fzCQmlZqmQqXfUCrulM1pPEMmICal2MDHXx4mtTgPNb37un+t9x/HTxGdoYIBdsoCwoERkG9gG3qqoAh4CPr3PcXcAe4HLgGuAeEbk2d+7TwKiqXgW8HfhTEdnm3xUZW4e7C38fPz1f9c9PpTMe19SWYRNQ66G9raVQ8y2TzXqEfbU4PbVIMhc76e1qo7uzrerf0chs3lD8TR87veBLtuUx17MZpGcoUAIKuBE4oKoHc8f3A7eLSKk4X2vcLcA+VU2p6hTwEHBHznp6B/ARAFUdBd4ETPp6RU3O1o1FAeXHwzU2uVhortbfE6ezvbWqn98MbB32LoDVxrP4beheY6SxGoO97YVsy8RKyhdXuVt5dCuV9aYuLj4ReTfwhVVOfRQ46joeBXqBHsDt5tu+xrjVzl0JXAycAP6TiLwLxzV4r6q+VNHFGGsykBMai4kkiZUU49OJqrr5jrs0flv8zo8tw9088/I44I+V63YfBWnxCwuRSIStw90FV/mxsfmqFtpdWk4xMesIvWgk4tkeUm/qYkGp6qOq2lL6H5ACVlOx0yXH0TXGlZ6L5F5vBXYBs6r6ZuA24BMi8sYKL8dYA+fhcmvoc1X9/OMBdU2EiS0uF9KpqcVCNetqkMlkPaWu7B6dH25PxGiVlQj3M7RxsDNQTSSD5uI7AmxxHW8FplS11O+w1rjSc1twrKjjueN9AKr6MvAEcC2Gr7i15mq6kM5Y/MyCOi8621sZ7HWytjKZbFUzxcanl1hJOvpld0droQ2LsT62uWO54/NV3bLh9UIES4EImoB6DLhORHbnju8G9q9z3H7gThFpEZF+HEvpEVU9DPwA+EUAEdkE3AB8z5crMQq4tb/jp6v3cJ2eXipo+z2dbbb4VcAW1wJYzUSJYyWxjXq3bwgr7vjq8kqa8Znq7YcKavwJAiagVHUM+ADwsIi8AFwBfAhARK4WkafONQ4nYeIV4GngAPCgqj6eO3cL8E4ReQ74GvBRVT1Qi2trZvq743TlH65kumqbDT3uvYBpfmHD64atngvJ64IN1uIXJvJxqDzHxqpzjxIrKcZdBWI3B+w5Ctw+KFV9FHh0lde/B+wtY1wKuOcsn30E+KmqTdYoi0gkwtaN3bx0xNnLcXRsriqVkkfHbPGrFu7F7+TEAql0hpZYZfprJpPluMWfqsa2jd0cPOo8Q6Nj81wlGyv+zBPjxcza4f6OwLWpCZQFZTQuO0Z6Cn+/evyMfdfrJpXOeDT9bRtNQFVCZ3srAz1OHCqdyVbFijo5ucDySjH+1N8dr/gzm5ltni0b81VJZnn1RPFZDOIzZALKqAk7NvUU4g8nJxdZWk5V9HnHxuYLhTMHetrps8WvYi7YXFQiXjtRuRLh/owLNvda/KlC+rrjhWSWVDrD6FhlGbHZbNZzj3Zu7q3o8/zABJRREzrbWwslbrLZLK+drGwBdL/fvbAa588FI8UF6tUTsxVvqn71RHEBDeLiF0Z2bSn+Ox4+PlPRZ41PJwotUOJtMU/3gaBgAsqoGTtdD1elbr4jJ4uLn3thNc6fLRu6CjGI2YWVivpDzS2uMJHLNItFI4F0H4WRnZv7Cn+/emKuIiXCreTt2NQbmPp7bkxAGTXDrUUfOTVH+jx720zPLTM97yyerbGoZfBViVgsyvZNrlhhBW4+t+to63B3oDZ/hplNg52FskeLiWRFFehf9bj3gumFMAFl1IzB3vbCXqWVZNqzQXA9uDW/bRu7iVWYbWYU2emyRiuJQ5XGn4zqEI1GPIre+SoRS8upgnCLRCLsCKgXwp5so2ZEIt6H65Vj5+dDdz+UO2zxqyrueN6J8QUSK+tPZkmmMhx1bQEwF2x1cT9Dh47NnJeb77WTxRij2yoLGiagjJpy0bb+wt+vjE4XKpGXy2Ii6dmkaMH36tLZ3lpoKJjJZjl8bP0a+qsnZjwZlvmOsEZ12DHSU9ijNjmbOK/q5gePTBf+dideBA0TUEZN2bKhq9Bld2k5xeip9aXKHj4+Syan+Y0MddFjvYWqzsUuJeKl3MbQ9fDy0eLit3tH/xojjfOhtSXGri3FZAk9sr57lFhOcdT13Lnvd9AwAWXUlEgkwu4dA4XjF19bXzuul1wP4+4AP1hhZvf2/sKepdGxeRYTybLfm1hJeVywu7fbPfIDuaD4DB08MrWu+pYHj057lLwg7yE0AWXUnEtcD9crx2bKXgCn55YLFQ4ikQgX2eLnC92dbYXMyGw2y4uvlq+hv3RkquC2HR7oKFSnMKrL9k09heKx80vJspMlstkszx2eKBzvCbiFawLKqDlDfR2FTYGZTJYXXi3PinKP2znSU3AVGtXn0l2Dhb+fPzxRViA+m83y/OHiPbps55AvczOcvWWX7izeo2dfGS/rfacmFwvFmltiUfa4vBlBxASUURcuv7C4eD1zcPyce6JS6QzPuzS/S3fZ4ucnF2/rJ97m7F2anl/mSBmxwhMTC97F74JgL35h53UXDhVcsUdPzXF66txdAp4+eLrw9+7t/bS3BTN7L48JKKMu7N7eX2jBsZBI8uJra7uRXnh1slC/r6ezzfbW+ExLLMolFxQ19B/q2Dnf88MXi2P27OgnHrDK2I1Gb1cbF20tJkv8QE+tOX56bpmXR4tbO664eINvc6sWJqCMuhCLRXn9nuHC8YHnT561OnMqneEHrsVv7+5hYgEsy9JovH73MFFXssTx8bNXOD89tcThXBwkEolUpRWEcW7eeMmmwt8Hj06vWVniu8+dKLhqd4z0sHGg8pY3fmMCyqgbl1845An0fv/F1TXAp146zdziCgAd8RYuu3Bw1XFGdentavME0b/59PFVY1HZbJZvPnOscHzhll5LjqgRwwMdXOiyor7+w9FVM/qOnZ7noCv9/5pLR2oyv0oxAWXUjbbWGG96XfFB+cGLY5yc8JY/Oj21xIHnTxaO3/S6EavrVkOuuWykYK2emlxcNRj/4qtTheaR0UiE6y7fXNM5Njs3XLHFc4+edD0v4Ox7+vKTRwrHu7f3B65z7tkIXIRMRG4GPgbEgWeAD6rqGTmUZxsnIjHgPuAmnOu7V1UfyL3nMuB/At1AFvhNVf1X/6/KOBuX7hxEX5vk+PgCmWyWLz5xmPe85UI2DnYyNZvgn755qJi23N/BZZYcUVP6uuPs3bOxYN1+8+njbOjrKHQwPjW5yNefGi2Mv/yiIQZ6zQ8lPloAAAtcSURBVHqqJf09ca65bITv/OgEAN974RQtsShvkI0sJpL807cOFzwQ8bYYb75ySz2nuy4CZUGJyDCwD7hVVQU4BHx8nePuAvYAlwPXAPeIyLW5c/8D+EtV3QvcCfydiAROSDcT0WiEt197QSFjLLGS4u+/epCHvqT87WNa6FfT2hLlxusuCGRLgEbn2ss2MdzfATjddr/wjUN8+9kTPPncSfZ//ZVC7LC/J871V4Rn8Wsk3iAbPZXov/OjE3xq/7P81aMveLL73n7NDrpDVH0lUAIKuBE4oKoHc8f3A7eLSOmqtNa4W4B9qppS1SngIeCO3LgYkM997QHWX8TKqDq9XW285y0XFXoRZbNZxqeXCrvdW2NR3n3DLotr1IlYLMo7r9tZiBem0hm+/+Ipnnz+JCtJp6V7e1sL77p+J60tQVtSmoNoNMK7rt/Jlg3FvlvJVKYQM4xEIvzk1ds9JZLCQF2sBxF5N/CFVU59FDjqOh4FenGEidvNt32NcauduzL3968CXxWRXwc2ArepamW9x42qsGmwk59/2x6+9oNRTyvrkaEu3nrV1lBkHDUy/T1xbvnxi/jnb73K5GzijHM3XbeTob6OOs3OACem+9NvvZAfvnSap146XahEv2mwkzdfuaXglg0TdRFQqvroat8tIr+NExsqJV1yHF1jXOm5CJAWkXbgc8D7VfWLInId8I8ickBVj67yWUaN6e+J8zM/dhHziytMzy/T09lGb1dbYTOiUV8Getp57zuEg0emOD4+TzYLmzd0ITsGrCdXQIjFolx96SbeeMlG5haTtLVGA78Zdy2CNvMjwJtcx1uBKVUt7Wx31nEicgRwO8K34FhRlwOdqvpFAFX9jog8l/scE1ABoruzLVR+8mYiFo1wyc5BLtlpqf5BJhKJFJqDhpmgqT2PAdeJyO7c8d3A/nWO2w/cKSItItIP3AY8ArwM9InIDQAichFwGfBDX67EMAzDqIhACShVHQM+ADwsIi8AVwAfAhCRq0XkqXONw0mYeAV4GjgAPKiqj6vqNE4CxZ+IyLPAw8Avq+orNbtAwzAMo2wi59MuuFkQkZ3A4a985Sts27at3tMxDMNoOEZHR3nb294GsEtVX3WfC5QFZRiGYRh5TEAZhmEYgcQElGEYhhFITEAZhmEYgSRo+6CCRgzg5MmT5xpnGIZhnAeu9fWMNgUmoNZmM8Dtt99e73kYhmE0OptxtggVMAG1NgeAtwAnOLPckmEYhlE5MRzhdKD0hO2DMgzDMAKJJUkYhmEYgcQElGEYhhFITEAZhmEYgcQElGEYhhFITEAZhmEYgcTSzMtERG4GPgbEgWeAD6rqbDljRCQG3AfchPNvfq+qPpB7z27gQWADMA+8T1VfDPk1/RTwVziNJfO8RVXn8JFKrsd1fjvwHeD1qjqee60u98jH66nL/SlnvmuNEZEO4JPAtTidsr8L/KqqLonIMPD/ARcAGZxWOt8K8fVcAXwbp49dnveqqgb4evpwnpNLcIyfv1LVP8i957yeIbOgyiD3498H3KqqAhwCPr6OMXcBe3C6+l4D3CMi1+bOfRZ4QFUvAz6M0+PK9x7nPl/TDTgCa6/rP7+FU6XXg4i8D/g63o7MUId75PP11Pz+lDPfMsb8Do4ydGXuvw7gt3LnPgl8I3eP7gD+XkQ6Q3w9NwB/U3KP/BZOlV7P7wGjqppfE/6DiFyfO3dez5AJqPK4ETigqgdzx/cDt5f8A6815hZgn6qmVHUKeAi4Q0S24mgbDwGo6j8D3cBVvl+RT9eUG3cD8JMi8rSIfENE3ur71VR4PSKyBfgZ4J3uD63jPfLlenLU4/6sOd8yx3wd+K+qmlHVNE437AtEpAX4P4BPAajqU8BBHOs+dNeTG3cDcKmI/EBEnhSRn/X5Ws4113LG/BrwG7nXN+NYWDOVPEMmoMpjO3DUdTwK9AI9ZY5Z7dy23OvHVTWzyjm/8euaACaAB4C9OBrh50XE72uq6HpU9biq/qyqvrTK59bjHvl1PVCf+wOVX9Nj+esRkQuAe4C/x3EbRVX1dMn7gn6PznY9AAvA3+JYIr8IPCAiV/txEeXMtZwxqppV1ZSIfAb4EfA1QKngGbIYVHlEgdVKbqTLHFN6LnKW193n/Mava0JV3dreEyLyLeAdOK4Bv6j0etbzubW4R35dT73uD1TpmkTkjcDngT9X1S/mrMXQ3qPS6wFQ1V9xjX1BRD4H/BTwvUonvQZVuR5VvUNE7gb+Afhd4F9XeU9Z98csqPI4gtePvxWYUtWFMseUntuCo0EcATaXmND5c37jyzWJSL+I/HbJNUWAZFVnfyaVXs9an1uPe+TL9dTx/kAVrklEbgO+BPymqv633JgxICIig673heIerXY9IhITkd8REbflEvhnSETemVMWUNV5HAvwDVTwDJmAKo/HgOtymSgAdwP71zFmP3CniLSISD9wG/CIqo7iZOm8F0BE3omTgfSsb1dS3nzLGbPqNQFzwK8CPwsgIlfhZCn9i18XUsZc1zPGQx3vkS/XQ/3uD1R4Tbnswz8FblTVv8m/QVVTwD8Bv5wbdyVwGY6LyU/8up408B6K13MBcCuOReInlf7mfh74cC4GGs8df7WSZ8iKxZaJiLwbJ7WyDack/PuAC4H/pap7zzZGVSdzQdx7cdwobcBfqOq9uffsxgnubgASOOmxPwj5NV0N/BmO7zoF/Lqq/luQr6fkc7LAsHrTzGt+j3y8nrrcn0qvSUQUGASOuT7ym6r6qyKyCfhfwC4cd9KHVPWxEF/PxThxwk041b4/oqqfC/j19OfmfHnu4z4PfFhVM+f7DJmAMgzDMAKJufgMwzCMQGICyjAMwwgkJqAMwzCMQGICyjAMwwgkJqAMwzCMQGKVJAzDJ0Tkn4G35A7jOOnPK7njb+AU3H0e2KqqMz7P5T3AO1T1P1bp89qArwI/k09fN4xqY2nmhlEDROTTwLSq3lOH7+7BKZFzg6pOVPFzbwVuUdU7zjnYMM4Ds6AMo06IyE7gMDAA9ANPAf8Fpw1DG05bgnTuuB2n8vUncu+9DKcKwRuBU8BH3dUISrgLeCIvnETkazjldX4Op2XK14CPAH8OXAo8Afy8qs6J0/vnD3FK2hwF/lBV/zr3uY8Afy4i4ncrCKM5sRiUYQSHPuBqYAdOCZlP4FSzvhD4APDfRWRARLpxBMy/ABuB/wv4hIi8ZdVPhV8C/q7ktV/BKXe0HXh97vwv4LR7uBh4v4hEcfr4/Iaq9gO/DvxZ7vvzJXm+kPt8w6g6JqAMI1j8nqquAF/BKXHzJ6q6DHwxd7wduBmYVdV7VTWpqgeAv8QRah5EZAQQ4MmSU/tU9XCuLNIPcWpDvpI7/jawK9ceYQ74hZzw+xowmCsEmuc7QK36SRlNhgkowwgW+RhRvhXBNICrl06UnJUjItP5/4D/m9X762wHErmmkqt9T/67pl3HGYprwztwEjz2595zn4i0usaeOMv3GkbFWAzKMIJFOVlLx4HvqWq+nTZn6YkEXmGzru8Rp2X6VlV9b87d92bgYeD7wGdyw2LUpn+Z0YSYBWUY4eNR4CIReX+u3ckuHPfbXauMPQq0icjQeXxPC/CFXM+ibO6zsoC7WvpmvB1WDaNqmIAyjJCRixPdBLwfOA18C6e1we+tMnYMp/329aXnyvieWZw+RL8FzALfxOn6+qhr2HU4CRuGUXVsH5RhNDgi8v8Al6rqB6r8ua3Aa8CPqerBan62YYBZUIbRDHwS+HERGa7y5/574MsmnAy/MAFlGA2Oqi7g7GH6aLU+M1fq6D8CH6rWZxpGKebiMwzDMAKJWVCGYRhGIDEBZRiGYQQSE1CGYRhGIDEBZRiGYQQSE1CGYRhGIPn/AUoH0lFelEjqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_results(results):\n",
    "    xs = results.V_out.index\n",
    "    ys = results.V_out.values\n",
    "\n",
    "    t_end = get_last_label(results)\n",
    "    if t_end < 10:\n",
    "        xs *= 1000\n",
    "        xlabel = 'Time (ms)'\n",
    "    else:\n",
    "        xlabel = 'Time (s)'\n",
    "        \n",
    "    plot(xs, ys)\n",
    "    decorate(xlabel=xlabel,\n",
    "             ylabel='$V_{out}$ (volt)',\n",
    "             legend=False)\n",
    "    \n",
    "plot_results(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And velocity as a function of time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The solver successfully reached the end of the integration interval.\n",
      "The solver successfully reached the end of the integration interval.\n",
      "The solver successfully reached the end of the integration interval.\n",
      "The solver successfully reached the end of the integration interval.\n",
      "The solver successfully reached the end of the integration interval.\n",
      "The solver successfully reached the end of the integration interval.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs = [1, 10, 100, 1000, 10000, 100000]\n",
    "for i, f in enumerate(fs):\n",
    "    system = make_system(Params(params, f=f))\n",
    "    ts = linspace(0, system.t_end, 301)\n",
    "    results, details = run_ode_solver(system, slope_func, t_eval=ts)\n",
    "    subplot(3, 2, i+1)\n",
    "    plot_results(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
